Methodology
How ASOTrendHQ turns public store data into research signals.
This page documents the inputs, update process, model interpretation, index-quality rules, and limitations behind ASOTrendHQ's app and keyword datasets.
Public inputs
App listings, rankings, ratings, categories, descriptions, and other information visible in Apple App Store and Google Play surfaces.
Independent estimates
Popularity, difficulty, search volume, traffic potential, and opportunity scores are model outputs—not official Apple or Google metrics.
Record-level freshness
App and keyword records expose their own last-refreshed timestamps. Missing source dates are omitted rather than replaced with the request time.
Quality-controlled indexing
Product records remain accessible, but only a scored primary cohort enters keyword sitemaps. Weak and duplicate pages use noindex, follow.
Collection and refresh
Scrapers collect public app-store listing data for both supported stores. Scheduled jobs refresh ratings, rankings, categories, keyword metrics, ranking histories, and derived analyses at different points in the daily processing chain. A record's displayed timestamp describes that record; it does not guarantee every upstream field changed.
Keyword estimates
Keyword popularity combines observed app competition and phrase characteristics. Difficulty weighs competing-app coverage and the quality of apps appearing near the top. Search volume and traffic potential are heuristic estimates derived from those signals. The values are useful for relative comparison inside ASOTrendHQ, but should not be interpreted as official monthly query counts.
Indexability and duplicates
The SEO cohort scores lexical quality, evidence, app coverage, ranking coverage, freshness, and enrichment. Normalized equivalent phrases are clustered, with one representative selected as canonical. The primary cohort is deliberately capped and can be expanded in measured batches after webmaster data supports doing so.
Known limitations
- Store interfaces, availability, and ranking results vary by country and time.
- Scraping or scheduled jobs can be delayed, incomplete, or temporarily unavailable.
- Models can over- or under-estimate demand and competitive difficulty.
- App-store data belongs to its respective publishers and platform providers.
- Important decisions should be cross-checked against store consoles and first-party analytics.
Citing the data
Cite ASOTrendHQ as the publisher, include the page URL and displayed refresh date, and describe modeled values as estimates. Stable reports are available in the research section.